'''simple docstring'''

import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
  from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image


class 					_lowercase						:
      '''simple docstring'''


      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_=13       ,lowerCamelCase_=64       ,lowerCamelCase_=2       ,lowerCamelCase_=3       ,lowerCamelCase_=True       ,lowerCamelCase_=True       ,lowerCamelCase_=32       ,lowerCamelCase_=5       ,lowerCamelCase_=4       ,lowerCamelCase_=37       ,lowerCamelCase_="gelu"       ,lowerCamelCase_=0.1       ,lowerCamelCase_=0.1       ,lowerCamelCase_=10       ,lowerCamelCase_=0.02       ,lowerCamelCase_=[1, 16, 4, 4]       ,lowerCamelCase_=None       ,)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[str] 					=	parent
         UpperCAmelCase__				:					Any 					=	batch_size
         UpperCAmelCase__				:					List[str] 					=	image_size
         UpperCAmelCase__				:					int 					=	patch_size
         UpperCAmelCase__				:					Optional[int] 					=	num_channels
         UpperCAmelCase__				:					Optional[int] 					=	is_training
         UpperCAmelCase__				:					Any 					=	use_labels
         UpperCAmelCase__				:					List[Any] 					=	hidden_size
         UpperCAmelCase__				:					Optional[int] 					=	num_hidden_layers
         UpperCAmelCase__				:					Tuple 					=	num_attention_heads
         UpperCAmelCase__				:					List[str] 					=	intermediate_size
         UpperCAmelCase__				:					Dict 					=	hidden_act
         UpperCAmelCase__				:					Tuple 					=	hidden_dropout_prob
         UpperCAmelCase__				:					Union[str, Any] 					=	attention_probs_dropout_prob
         UpperCAmelCase__				:					Tuple 					=	type_sequence_label_size
         UpperCAmelCase__				:					List[str] 					=	initializer_range
         UpperCAmelCase__				:					Union[str, Any] 					=	scope
         UpperCAmelCase__				:					List[str] 					=	backbone_featmap_shape

         # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         # the number of patches is based on the feature map of the backbone, which by default uses an output stride
         # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
         UpperCAmelCase__				:					Dict 					=	(self.image_size // 32) ** 2
         UpperCAmelCase__				:					Dict 					=	num_patches + 1




      def 		lowerCAmelCase__       (					self				)      ->      Tuple:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

         UpperCAmelCase__				:					Optional[Any] 					=	None
         if self.use_labels:
            UpperCAmelCase__				:					int 					=	ids_tensor([self.batch_size]       ,self.type_sequence_label_size				)

         UpperCAmelCase__				:					Optional[int] 					=	self.get_config()

         return config, pixel_values, labels




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	{
             '''global_padding''': '''same''',
             '''layer_type''': '''bottleneck''',
             '''depths''': [3, 4, 9],
             '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
             '''embedding_dynamic_padding''': True,
             '''hidden_sizes''': [4, 8, 16, 32],
             '''num_groups''': 2,
         }

         return ViTHybridConfig(
             image_size=self.image_size       ,patch_size=self.patch_size       ,num_channels=self.num_channels       ,hidden_size=self.hidden_size       ,num_hidden_layers=self.num_hidden_layers       ,num_attention_heads=self.num_attention_heads       ,intermediate_size=self.intermediate_size       ,hidden_act=self.hidden_act       ,hidden_dropout_prob=self.hidden_dropout_prob       ,attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,is_decoder=__snake_case       ,initializer_range=self.initializer_range       ,backbone_featmap_shape=self.backbone_featmap_shape       ,backbone_config=__snake_case       ,)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	ViTHybridModel(config=__snake_case				)
         model.to(__snake_case				)
         model.eval()
         UpperCAmelCase__				:					Optional[int] 					=	model(__snake_case				)
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_       ,lowerCamelCase_				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.type_sequence_label_size
         UpperCAmelCase__				:					List[str] 					=	ViTHybridForImageClassification(__snake_case				)
         model.to(__snake_case				)
         model.eval()
         UpperCAmelCase__				:					Any 					=	model(__snake_case       ,labels=__snake_case				)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.type_sequence_label_size)				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					List[Any] 					=	self.prepare_config_and_inputs()
         UpperCAmelCase__					,   UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[Any] 					=	config_and_inputs
         UpperCAmelCase__				:					List[str] 					=	{'''pixel_values''': pixel_values}
         return config, inputs_dict




@require_torch
class 					_lowercase						(   snake_case__		,snake_case__		,unittest.TestCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   str   				= (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
      UpperCAmelCase_   :   Dict   				= (
          {'''feature-extraction''': ViTHybridModel, '''image-classification''': ViTHybridForImageClassification}
          if is_torch_available()
          else {}
      )
      UpperCAmelCase_   :   Optional[int]   				= False
      UpperCAmelCase_   :   Optional[int]   				= False
      UpperCAmelCase_   :   List[Any]   				= False




      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	ViTHybridModelTester(self				)
         UpperCAmelCase__				:					Any 					=	ConfigTester(self       ,config_class=__snake_case       ,has_text_modality=__snake_case       ,hidden_size=37				)




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         self.config_tester.run_common_tests()




      @unittest.skip(reason='''ViT does not use inputs_embeds'''				)
      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         pass




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[Any] 					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase__				:					Union[str, Any] 					=	model_class(__snake_case				)
            self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module)				)
            UpperCAmelCase__				:					Optional[Any] 					=	model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__snake_case       ,nn.Linear				)				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					Optional[Any] 					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            UpperCAmelCase__				:					Dict 					=	model_class(__snake_case				)
            UpperCAmelCase__				:					Tuple 					=	inspect.signature(model.forward				)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            UpperCAmelCase__				:					int 					=	[*signature.parameters.keys()]

            UpperCAmelCase__				:					int 					=	['''pixel_values''']
            self.assertListEqual(arg_names[:1]       ,__snake_case				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case				)




      def 		lowerCAmelCase__       (					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase__				:					Any 					=	_config_zero_init(__snake_case				)
         for model_class in self.all_model_classes:
            UpperCAmelCase__				:					Optional[int] 					=	model_class(config=__snake_case				)
            # Skip the check for the backbone
            for name, module in model.named_modules():
               if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                  UpperCAmelCase__				:					Optional[Any] 					=	[f'''{name}.{key}''' for key in module.state_dict().keys()]
                  break

            for name, param in model.named_parameters():
               if param.requires_grad:
                  if name in backbone_params:
                     continue
                  self.assertIn(
                      ((param.data.mean() * 1e9).round() / 1e9).item()       ,[0.0, 1.0]       ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''       ,)




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Dict:



         '''simple docstring'''



         for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase__				:					Any 					=	ViTHybridModel.from_pretrained(__snake_case				)
            self.assertIsNotNone(__snake_case				)







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Union[str, Any] 					=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
   return image


@require_torch
@require_vision
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      @cached_property
      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         return (
             ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
             if is_vision_available()
             else None
         )




      @slow
      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(
             __snake_case				)

         UpperCAmelCase__				:					Tuple 					=	self.default_image_processor
         UpperCAmelCase__				:					Any 					=	prepare_img()
         UpperCAmelCase__				:					Dict 					=	image_processor(images=__snake_case       ,return_tensors='''pt'''				).to(__snake_case				)

         # forward pass
         with torch.no_grad():
            UpperCAmelCase__				:					int 					=	model(**__snake_case				)

         # verify the logits
         UpperCAmelCase__				:					Any 					=	torch.Size((1, 1000)				)
         self.assertEqual(outputs.logits.shape       ,__snake_case				)

         UpperCAmelCase__				:					Tuple 					=	torch.tensor([-1.9090, -0.4993, -0.2389]				).to(__snake_case				)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,__snake_case       ,atol=1e-4				)				)




      @slow
      @require_accelerate
      def 		lowerCAmelCase__       (					self				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''				)
         UpperCAmelCase__				:					str 					=	ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''       ,device_map='''auto'''				)

         UpperCAmelCase__				:					Tuple 					=	prepare_img()

         UpperCAmelCase__				:					Optional[Any] 					=	image_processor(images=__snake_case       ,return_tensors='''pt'''				)
         UpperCAmelCase__				:					Any 					=	model(**__snake_case				)
         UpperCAmelCase__				:					Tuple 					=	outputs.logits
         # model predicts one of the 1000 ImageNet classes
         UpperCAmelCase__				:					Dict 					=	logits.argmax(-1				).item()

         self.assertTrue(model.config.idalabel[predicted_class_idx]       ,'''tabby, tabby cat'''				)








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




